optical-quantum-kernel

Original source / Raw SKILL.md

---
name: optical-quantum-kernel
description: Simulates a quantum kernel using optical fiber storage and linear optics.
author: tempguest
version: 0.1.0
license: MIT
---

# Optical Quantum Kernel Skill

This skill simulates a photonic quantum computer that uses optical fibers for storage and linear optics for computation.
It calculates the quantum kernel (similarity) between two data vectors by encoding them into optical phases, passing them through simulated fibers (with loss), and interfering them.

## Security Features
- **Resource Bounding**: Capped at 8 modes to prevent resource exhaustion.
- **Input Validation**: Strict checks on input vector dimensions and limits.
- **Physics-Based Constraints**: Includes attenuation and phase noise for realism.

## Commands

- `simulate`: Run the quantum kernel simulation on two input vectors.


---

## Skill Companion Files

> Additional files collected from the skill directory layout.

### README.md

```markdown
# Optical Quantum Kernel Simulation

This skill simulates a "Quantum Kernel Trick" using a physics-based model of an optical quantum computer.

## The Physics Model
1.  **Optical Fiber Storage**: The quantum state (photons) travels through optical fibers.
    -   **Loop**: Storage is simulated as a fiber loop.
    -   **Loss**: Attenuation ($0.2 dB/km$) reduces the signal amplitude.
    -   **Noise**: Random phase drift simulates thermal fluctuations in the fiber.
2.  **Linear Optics**: 
    -   **Encoding**: Data is mapped to the phase of optical modes.
    -   **Computation**: A 50:50 Beam Splitter interferes the modes.
3.  **Measurement**: Photodetectors measure the intensity at the output ports to estimate the kernel (similarity).

## Security Features
- **Bounded Resources**: The simulation acts as a "Sandbox", strictly creating only up to 8 optical modes/qubits. This prevents Denial of Service (DoS) via memory exhaustion.
- **Input Sanitization**: Verifies that inputs are valid numerical vectors before processing.

## Usage

Calculate the kernel (similarity) between two vectors:

```bash
python3 scripts/optical_kernel.py "0.5,1.2" "0.5,1.2"
# Expected: High kernel value (~1.0)
```

Orthogonal/Different vectors:
```bash
python3 scripts/optical_kernel.py "0.1,0.2" "0.9,0.8"
# Expected: Lower kernel value
```

## Publishing to ClawHub

To publish this skill:
```bash
clawhub publish
```

```

### _meta.json

```json
{
  "owner": "aadipapp",
  "slug": "optical-quantum-skill",
  "displayName": "Tenqua OpticalQuantumSkill",
  "latest": {
    "version": "1.0.0",
    "publishedAt": 1771220985774,
    "commit": "https://github.com/openclaw/skills/commit/5e5b469772f31b403c7045fc424e88ed66180859"
  },
  "history": []
}

```

### scripts/optical_kernel.py

```python
#!/usr/bin/env python3
import numpy as np
import argparse
import sys

class OpticalQuantumSimulator:
    def __init__(self, num_modes=2, fiber_length_km=1.0, attenuation_db_km=0.2):
        # Security: Resource Bounding
        if num_modes > 8:
             raise ValueError("Security Violation: Simulation limited to 8 modes to prevent resource exhaustion.")
        self.num_modes = num_modes
        self.state = np.zeros(num_modes, dtype=complex)
        
        # Fiber properties
        self.fiber_length = fiber_length_km
        self.attenuation = attenuation_db_km # dB/km
        
    def encode_data(self, data_vector):
        """Encodes data into optical phases (Phase Encoding)."""
        # Security: Input Validation
        if len(data_vector) > self.num_modes:
            raise ValueError(f"Input vector dimension {len(data_vector)} exceeds available modes {self.num_modes}")
        
        # Normalize data to surfactant phases [0, 2pi]
        norm = np.linalg.norm(data_vector)
        if norm > 0:
            phases = (data_vector / norm) * 2 * np.pi
        else:
            phases = data_vector

        # Initialize state with amplitude 1.0 (mean photon number)
        for i, phase in enumerate(phases):
            self.state[i] = np.exp(1j * phase)

    def pass_through_fiber(self):
        """Simulates storage in optical fiber: Attenuation and Phase Drift."""
        # 1. Attenuation: Loss = 10^(-alpha * L / 10)
        transmission = 10 ** (-self.attenuation * self.fiber_length / 10)
        amplitude_damping = np.sqrt(transmission)
        
        # 2. Phase Drift: Random fluctuations due to thermal/stress
        # In a real fiber, this is time-dependent. We simulate a snapshot.
        phase_drift = np.random.normal(0, 0.1, self.num_modes) # std dev 0.1 rad
        
        # Apply effects
        damping_matrix = np.diag([amplitude_damping] * self.num_modes)
        phase_matrix = np.diag(np.exp(1j * phase_drift))
        
        # Evolution
        self.state = phase_matrix @ damping_matrix @ self.state

    def interference_step(self):
        """Interferes modes using a 50:50 Beam Splitter (Hadamard-like for 2 modes)."""
        if self.num_modes == 2:
            # Standard 50:50 BS matrix
            bs_matrix = (1/np.sqrt(2)) * np.array([[1, 1j], [1j, 1]])
            self.state = bs_matrix @ self.state
        else:
            # For >2 modes, we could implement a DFT-like interference (Multiport BS)
            # Implementing a simplified cascade for demonstration
            # Here we just mix adjacent modes
            for i in range(0, self.num_modes - 1, 2):
                sub_state = self.state[i:i+2]
                bs = (1/np.sqrt(2)) * np.array([[1, 1j], [1j, 1]])
                self.state[i:i+2] = bs @ sub_state

    def measure_intensity(self):
        """Measures photon number (intensity) in each mode."""
        return np.abs(self.state)**2

def compute_kernel(vec_a, vec_b):
    """
    Computes a 'Quantum Kernel' similarity between two vectors via optical simulation.
    This is a simplified model inspired by the SWAP test or interference dip visibility.
    """
    sim = OpticalQuantumSimulator(num_modes=2)
    
    # Validation
    if len(vec_a) != len(vec_b):
         raise ValueError("Vectors must have same dimension")

    # To simulate kernel K(a, b) = |<a|b>|^2, we can encode 'a' in mode 0 and 'b' in mode 1
    # BUT, physically, we usually prepare two separate states and interfere them.
    # Here, we will map scalars to phases in a 2-mode system for a single feature dimension demo,
    # OR map vectors to temporal modes.
    # Simplest for this demo skill: 
    #   Encode scalar x in mode 0 phase, scalar y in mode 1 phase.
    #   Interfere on BS. Detect output.
    #   If x == y, constructive/destructive interference happens predictably.
    
    # Let's perform element-wise kernel estimation for vectors
    kernel_sum = 0
    
    for i in range(len(vec_a)):
        val_a = vec_a[i]
        val_b = vec_b[i]
        
        # Reset simulator for each component pair
        sim = OpticalQuantumSimulator(num_modes=2)
        
        # Encode: Mode 0 gets val_a, Mode 1 gets val_b
        # We need to manually set the state because encode_data sets all modes from one vector
        # Phase encoding:
        phi_a = val_a # Simplified mapping
        phi_b = val_b
        
        sim.state[0] = np.exp(1j * phi_a)
        sim.state[1] = np.exp(1j * phi_b)
        
        # Storage
        sim.pass_through_fiber()
        
        # Interference
        sim.interference_step()
        
        # Measurement
        intensities = sim.measure_intensity()
        
        # For a 50:50 BS with inputs exp(i*phi_a) and exp(i*phi_b):
        # Out1 ~ exp(i*phi_a) + i*exp(i*phi_b)
        # Intensity1 ~ 1 + 1 + 2*sin(phi_b - phi_a) ... depends on BS convention.
        # A common kernel measure is visibility or simply the overlap.
        # Let's use a classical proxy for the overlap computed from intensities.
        # Perfect overlap (phi_a = phi_b) -> constructive in one port, destructive in other (depends on phase shift).
        
        diff = intensities[0] - intensities[1]
        kernel_sum += np.exp(-abs(diff)) # Heuristic metric for similarity

    return kernel_sum / len(vec_a)

def main():
    parser = argparse.ArgumentParser(description="Optical Quantum Kernel Simulator")
    parser.add_argument("vector_a", type=str, help="Comma-separated vector A (e.g. 0.1,0.5)")
    parser.add_argument("vector_b", type=str, help="Comma-separated vector B (e.g. 0.1,0.5)")
    
    args = parser.parse_args()

    try:
        vec_a = [float(x) for x in args.vector_a.split(",")]
        vec_b = [float(x) for x in args.vector_b.split(",")]
        
        print(f"Simulating Quantum Kernel Estimation...")
        print(f"Device: Optical Fiber Storage + Linear Optics")
        print(f"Security: Resource Bound (8 modes), Input Validation (Numeric)")
        
        kernel_val = compute_kernel(vec_a, vec_b)
        
        print(f"\nEstimated Kernel Value: {kernel_val:.4f}")
        
    except ValueError as e:
        print(f"Error: {e}")
        sys.exit(1)

if __name__ == "__main__":
    main()

```